A Probabilistic Approach to Personalized Tag Recommendation - PowerPoint Presentation

A Probabilistic Approach to Personalized Tag Recommendation
A Probabilistic Approach to Personalized Tag Recommendation

A Probabilistic Approach to Personalized Tag Recommendation - Description


Meiqun Hu EePeng Lim and Jing Jiang School of Information S ystems Singapore Management U niversity 1 Social tagging allows users to annotate resources with tags organize tags are keywords serving as personalized index terms that group relevant resources ID: 510205 Download Presentation

Tags

alice scenario translation tag scenario alice tag translation recommendation users tags user foto

Embed / Share - A Probabilistic Approach to Personalized Tag Recommendation


Presentation on theme: "A Probabilistic Approach to Personalized Tag Recommendation"— Presentation transcript


Slide1

A Probabilistic Approach to Personalized Tag Recommendation

Meiqun Hu, Ee-Peng Lim and Jing JiangSchool of Information SystemsSingapore Management University

1Slide2

Social tagging allows

users to annotate resources with tags.organizetags are keywords, serving as (personalized) index terms that group relevant resources

store

online storage gives mobility and convenience to accesssharepublished bookmarks can be viewed by other usersexploreto leverage collective wisdom to find interesting resources

Social Tagging

Image credit @ logorunner.com

2Slide3

Personalized tag recommendation aims to recommend tags to the query user for annotating the query resource.

Recommendation eases the tagging process.avoids misspelling, provides consistency

Personalized Tag Recommendation

?

Alice

3Slide4

Tag recommendation should be personalized.

users exhibit individualized choice of tag termse.g., language preferencepersonalized index for personal consumption and consistency

Why Personalize Recommendations?

Alice

4Slide5

Problem Formulation: p(

t|rq,uq)A Basic Method: freq-r, to recommend most frequent tags

assuming that

the more people have used this tag, the more likely it will be used againRef. [Golder & Huberman 2006]current state-of-the-art in many social tagging sites, e.g.,

fails to personalize the recommendations for the query user

Problem Formulation and A Basic Method

5Slide6

Scenario 1: ‘

foto’ is an infrequent tag for the resource.

Scenario 2: ‘

foto’ has not been used for the resource, but has been used by the user for annotating other resources in the past.Scenario 3: ‘foto

’ has not been used for the resource, neither has it been used by the query user, but has been used by other users for annotating other resources.

Three Scenarios

Alice

6Slide7

A Method based on Collaborative Filtering: (

knn)select the k-nearest neighbors of the query user, and recommend tags used by these neighbors for annotating the resource

classic collaborative filtering, without ratings

Ref. [Marinho & Schmidt-Thienme 2008]addresses scenario 1, but fails scenario 2,3Collaborative Filtering Method

7Slide8

To translate the resource tags to the user’s personal tags (

trans-u)to learn p(t=‘foto’|u=Alice, tr=‘photo’)

Ref. [

Wetzker et al. 2009]addresses scenario 2, but fails scenario 3, since Alice has never used ‘foto

Personomy Translation Method

Alice

8Slide9

To Address Scenario 3

Alice

Bob

Alice

borrow translation

9Slide10

A Probabilistic Framework

Personomy TranslationA FrameworkMeasuring User Similarity

10Slide11

To learn p(t=‘foto’|u=Bob,t

r=‘photo’) and sim(u=Bob,uq=Alice)

Proposed Framework

Alice

Bob

borrow translation

11Slide12

To learn p(t=‘foto’|u=Alice,tr

=‘photo’)Personomy Translation

[Wetzker et al. 2009]

Alice

12Slide13

sim(u,uq

)assuming that users are similar if they perform similar translationsUser profileMeasuring Similarity between Users

photo

web

foto

image

netz

internet

13Slide14

Distributional Divergence between Users

sim

(‘photo’)

(

u,u

q

)

sim

(‘web’)

(

u,u

q

)

S

tr

sim(u,u

q

)

Ref. [Lee 1997]

14Slide15

This framework is able to address all three scenarios

addresses scenario 1 by allowing self-translation, e.g., p(‘photo’|u,‘photo’)addresses scenario 2 by allowing the query to be most similar to himeself, e.g., sim(u

q

,uq)addresses scenario 3 by enabling borrowed translationsRemark on the 3 Scenarios

15Slide16

Experiments

Data CollectionExperimental SetupRecommendation Performance

16Slide17

train

validationtest

time frame

start ~ DEC 08JAN 09 ~ JUL 09

JUL 09 ~ DEC 09

number of resources22,389

667258number

of users1,185136

57number of tags

13,276862525

number

of assignments

253,615

2,604

1,262

average posts per user

53.695

5.699

4.895

average tag tokens

per user

3.955

3.360

4.523

average distinct tags per user

61.833

13.191

14.667

Dataset from BibSonomy

Note:

users in test set must have been appeared in validation set.

17Slide18

Methods to compare

trans-n1, trans-n2trans-u1, trans-u2[Wetzker

et al. 2009], [

Wetzker et al. 2010]knn-ur, knn-ut

interpolating with freq-

rEvaluation metric

pr-curve at top 5macro-average for usersParameter tuningmacro-average f1@5

global vs. individual settings

Experimental Setup

18Slide19

Recommendation Performance

Global Setting

19Slide20

Recommendation Performance

Individual Setting

20Slide21

u

rtags assigned

top 5 recommendations

trans-u1

trans-n1

freq

-r920

a45…57f2008, bookmarking, folksonomy, social, spam,

folksonomies, tagorapub, web20, 20, integpub

, systems, tagger, webdiplomathesiscaptchafolksonomybackground

closelyrelated

folksonomy

folksonomy

tagging

social

web20

web

spam

social

myown

mining

folksonomy

1119

d16…b50

it, news, technology, blog, feed,

technologie

kultur

online

radio

kunst

cd

news

web20

blog

software

technology

newsticker

news

pc

langde

heise

3217

467…655

annotation, ontology, knowledge, semantic

sql

erd

eclipse

tagging

folksonomy

ontology

web20

semantic

tools

survey

smilegroup

semantics

ontology

Recommendation Case Study

scenario 3 tags

21Slide22

We propose a probabilistic framework for solving the personalized tag recommendation task, which incorporate

personomy translation and borrowing translation from neighbors.We devise to use distributional divergence to measure similarity between users. Users are similar if they exhibit similar translation behavior.We find the proposed methods give superior performance than translation by the query user only and classic collaborative filtering.

Conclusion

22Slide23

Performance gain in successfully recommending scenario 3 tags.

e.g., compared with freq-re.g., resources that are inadequately taggedRecommendations strategies from the resources’ perspective.

Future Work

Thank you

23Slide24

[

Golder & Huberman 2006]

Scoot A.

Golder and Bernardo A. Huberman. Usage

Patterns of Collaborative Tagging Systems. Journal of Information Science, 32(2):198-208, 2006.

[Maronho & Schmidt-

Theime 2008]

Leandro B. Marinho and Lars Schmidt-Thieme

. Collaborative Tag Recommendations, Chater 63, pp. 533-540. Springer Berlin Heidelberg, 2008.

[Wetzker et al. 2009]

Robert

Wetzker

, Alan Said and

Carsten

Zimmermann. Understanding the User:

Peronomy

Translation for Tag Recommendation. In ECML PKDD Discovery Challenge, pp. 275-285, 2009.

[Lee 1997]

Lillian

Lee. Similarity-Based Approaches to Natural Language Processing.

Ph.D

Thesis, Harvard University, Cambridge, MA. 1997. Chapter Four.

Reference

24

Shom More....
By: mitsue-stanley
Views: 38
Type: Public

Download Section

Please download the presentation from below link :


Download Presentation - The PPT/PDF document "A Probabilistic Approach to Personalized..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Try DocSlides online tool for compressing your PDF Files Try Now